- #GARTNER HYPE CYCLE FOR EMERGING TECHNOLOGIES 2018 FULL#
- #GARTNER HYPE CYCLE FOR EMERGING TECHNOLOGIES 2018 SERIES#
For example, a training set of 1,000 training items and 200 evaluation items will produce different results than that of 100 training and 20 evaluation. These variables cannot be easily accounted for when they create a dynamic output model. These sets can be of varying length and quality. DL requires algorithmic training, and regularly utilizes a training and evaluation data set. Yesterday’s neural net may be different from today’s, and a medical device company may have challenges producing versioning records for a regularly changing system.Īnother challenge posing the regulatory system is that of standards requirements. With continuous learning, there is no longer a set point in time that can be inspected against. Scott Gottlieb, the 23rd Commissioner of FDA, is working to establish a regulatory framework to handle these changes. The FDA is also working to tackle another challenge of machine learning: continuous learning. These programs still have a significant line of development before they are fully realized. These systems are currently being developed, with FDA slated to release a pilot program to pre-certify machine learning programs by the end of 2018. As a result, it’s hard for regulators to currently adequately define validation of DL for medical applications. It also provides a challenge for a practitioner’s understanding of Why an automated diagnosis was made. The ‘black box’ nature of machine learning poses a problem then for FDA, Notified Bodies, and other regulatory agencies. These fittings are a black box of re-fit equations and cannot provide an exact ‘ground truth.’ They also cannot provide “Why” a given result was spat out.
#GARTNER HYPE CYCLE FOR EMERGING TECHNOLOGIES 2018 SERIES#
DL algorithms are beyond the scope of this article but are a series of mathematical formulas iterated repeatedly to slowly better fit a data set. Even a computer simulation has a ground truth set of variables. Regulations and quality systems, along with the testing, evaluation, inspection, and the validation of products, have historically been based off some form of ground truth. Machine learning, on the other hand, is an anathema to the way regulations have historically been designed.
#GARTNER HYPE CYCLE FOR EMERGING TECHNOLOGIES 2018 FULL#
They are designed to keep patients safe and ensure full traceability is provided for a device’s safety and efficacy. The world of regulations is slow to move, and for good reason. Despite these promises, one of the large challenges posing deep learning in healthcare is that of managing its implementation and regulations. These assessments could then be leveraged using models into medical devices to produce a diagnosis, patient monitoring, and continuous improvement of patient care to respond appropriately to a patient’s changes. Several deep learning applications being leveraged are diagnosing cancer, assessing stroke severity, and monitoring heart disease. Despite its current buzzword state, deep learning (DL) is an immensely powerful tool and will continue to expand in applications, which brings us to the medical device world.
Technology buzzwords seem to come-and-go at an increasing rate, so trying to determine which trends will make lasting impacts can be a challenge. It probably no surprise that year over year the Emerging Technologies Hype Cycle is extremely popular, it’s the most read hype cycle from Gartner and one of the longest running hype cycles.Post Created by Max Burton. Can provide insight on how innovative your vendors are.Future proofing solutions with ensuring the use of the most impactful technologies.Can provide project de-risking if teams review emerging technologies that may eliminate the need for or consolidate technologies proposed for a project.Provides a candidate list of high-potential innovation program pilots/prototypes.Aids senior executives with vital input into their long and short-range strategy and planning.